Yes. Some companies say AI coding tools have made it feasible to build software internally instead of buying a product or feature. In McKinsey’s 2026 global survey, 32% of respondents said their organizations had decided against at least one software purchase for that reason. That measures reported decisions—not a verified share of software replaced or proof that vendors are being permanently displaced.
What the 32% figure does—and does not—mean
McKinsey’s 2026 global survey asked respondents about organizational decisions not to buy one or more software products or features because their organization could build them internally with agentic coding tools. The reported 32% is the share of respondents answering that way. It is not the share of companies that have replaced all SaaS, the percentage of software products displaced, or an independently audited count of canceled contracts.
The survey also points to a gap between experimentation and broad financial returns: 37% of respondents said AI had contributed at least some EBIT impact to their organization, a share McKinsey reported as essentially unchanged from the prior year. About 20% said AI-related operating costs constrained their organization’s AI use. These are separate survey responses, not evidence that internal software builds caused either outcome.
Companies still buy software and ready-to-use AI
Building is one sourcing option, not a wholesale switch away from vendors. In the UK government’s 2025 AI Adoption Research, 3,500 businesses were interviewed between 12 February and 2 May. Sixteen percent reported currently using at least one AI technology. Among businesses using particular technologies, external ready-to-use solutions were more common than in-house development: for natural-language processing or text generation, 71% bought external software or ready-to-use systems and 14% developed in-house; for machine learning, the figures were 55% and 24%, respectively.
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Those percentages apply to businesses using each technology, not all UK businesses, and the survey predates McKinsey’s 2026 result. It examines AI adoption routes rather than decisions to forgo general-purpose software purchases, so it is useful context—not a direct comparison.
Earlier evidence shows the same mixed approach. The OECD, BCG and INSEAD survey of AI-adopting enterprises in G7 countries found that more than 70% of enterprises in both ICT and manufacturing reported conducting AI research and development for their own use. At the same time, 53–64% relied on customized third-party systems or purchased off-the-shelf software or hardware. That 2022–23 survey describes a defined group of adopters and is older context, not a current measure of agentic coding.
What companies are building
Reported internal builds tend to target particular workflows rather than replicate an entire enterprise software suite. In EY’s AI Pulse Survey Wave 5, among senior leaders at organizations investing in AI whose organizations were piloting or had fully deployed AI development for internal use, the most frequently cited category was team-specific workflow and productivity tools (60%). Other cited categories included experimental tools (39%), AI enhancements to existing enterprise software (39%), and replacements for existing enterprise software (33%).
The same EY respondents cited tools that had previously seemed too resource-intensive (33%), too time-intensive (31%), or too niche to be economically viable (29%). These are reported categories for that particular cohort, not verified deployment rates across businesses.
Retool’s 2026 Build vs. Buy Report adds a more narrowly sourced signal: in a late-2025 survey of 817 Retool customers and builders, 35% said they had replaced at least one SaaS tool with a custom build, while 78% expected to build more custom internal tools in 2026. The first is a respondent report; the second is an expectation, not a completed result. Retool also found workflow automation and internal administration among the SaaS categories facing replacement pressure, alongside CRM, business intelligence, project management, and customer support. Because Retool sells internal-tool software and surveyed its customers and builders, those findings are not a neutral census of the market.
Why build—and why keep buying?
When an internal build can make sense
A custom tool may fit a team’s distinctive process more closely, connect internal systems or data in a specific way, or let staff test an idea that once seemed too expensive or time-consuming to pursue. EY’s findings on workflow tools and previously impractical projects reflect those use cases. A build can also reduce dependence on a vendor for a particular function, but that does not automatically reduce total cost: the company takes on responsibility for the tool’s operation and upkeep.
Why an off-the-shelf product can still win
Businesses interviewed for the UK government study cited limited technical expertise, uncertainty about what they wanted to build, and the significant cost of software development as obstacles. One small-business interviewee in construction, currently using AI, put the trade-off this way: “With any software development there will be fairly significant cost, whereas if you buy something off the shelf, you can pick it up and drop it.” Ready-to-use products can be easier to adopt—and easier to discontinue—than an internally maintained application.
AI does not remove the costs of operating a software product. McKinsey’s survey found AI-related operating costs constrained usage for about one-fifth of respondents. EY also raises the practical question of who will maintain, govern, and secure internally built tools. Faster creation is useful only if a company can support what it creates.
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There is no universal winner. Compare the real product with a realistic internal alternative across the full lifecycle, not just the time needed to generate an initial version.
- Fit and distinctiveness: Is the workflow unusually important to how the team competes, or is it a standard capability that a mature product already handles well?
- Total lifecycle cost: Include AI usage, engineering review, integrations, security work, maintenance, upgrades, and the cost of keeping an accountable owner available. Do not compare a quick prototype with a recurring subscription as if the prototype were a finished, supported product.
- Time to value: Would a vendor product be ready sooner, or does its poor fit and procurement process create more delay than a focused internal tool?
- Data and integration: Identify which systems the tool must connect to, who can access sensitive data, and how permissions will be enforced.
- Skills and ownership: Name who will review the code, document it, respond to failures, and maintain it after the original builder moves on.
- Risk and governance: Before wider use, assess security, privacy, compliance, auditability, reliability, and change control.
Retool’s 2026 survey also found that 60% of its respondents said they had built software outside IT oversight in the prior year, and 25% said they did so frequently. Those figures describe Retool’s surveyed customers and builders, not all companies; they nonetheless illustrate why ownership and review matter when quick experiments become business-critical tools.
What the evidence cannot establish
There is no directly comparable survey measure here showing what share of all companies permanently replaced purchased software with internally built AI software. The available findings use different populations, dates, and questions: a global survey about decisions not to buy, UK business research on AI sourcing, a G7 survey of AI adopters, and vendor- or cohort-specific reports. They support a change in the build-versus-buy calculation, not a conclusion that enterprise software vendors have become obsolete.
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